Improvement of tissue survival of skin flaps by 5α-reductase inhibitors: Possible involvement of nitric oxide and inducible nitric oxide synthase

Background: Skin flap grafting is a popular approach for reconstruction of critical skin and underlying soft tissue injuries. In a previous study, we demonstrated the beneficial effects of two 5α-reductase inhibitors, azelaic acid and finasteride, on tissue survival in a rat model of skin flap grafting. In the current study, we investigated the involvement of nitric oxide and inducible nitric oxide synthase (iNOS) in graft survival mediated by these agents. Methods: A number of 42 male rats were randomly allocated into six groups: 1, normal saline topical application; 2, azelaic acid (100 mg/flap); 3, finasteride (1 mg/flap); 4, injection of L-NG-nitroarginine methyl ester (L-NAME) (i.p., 20 mg/kg); 5, L-NAME (20 mg/kg, i.p.) + azelaic acid (100 mg/flap, topical); 6, L-NAME (20 mg/kg, i.p.) + finasteride (1 mg/flap, topical). Tissue survival, level of nitric oxide, and iNOS expression in groups were measured. Results: Our data revealed that azelaic acid and finasteride significantly increased the expression of iNOS protein and nitric oxide (NO) levels in graft tissue (P < 0.05). These increases in iNOS expression and NO level were associated with higher survival of the graft tissue. Conclusion: It appears that alterations of the NO metabolism are implicated in the azelaic acid- and finasteride-mediated survival of the skin flaps. © 2015, Pasteur Institute of Iran. All rights reserved

Chenopodium khorasanica (Amaranthaceae), a new species from Iran

Chenopodium khorasanica (Amaranthaceae) is described as new species from north-east of Khorasan province (Iran). The new species is compared with its closest relative Chenopodium sosnowskyi Kappeler and Chenopodium vulvaria L.. This species is similar to Ch. sosnowskyi in having hair shape of leaf, habit and petiole size. Ch.khorasanica differs from Ch. sosnowskyi in having stem height, hair length of leaf, hairs density surface of leaf, inflorescence height, number of flowers in gap, ornamentation of surface cell of seed. This species similar to Ch. vulvaria in having hair shape of leaves, petiole length and without stomata of perianth surface leaf margin and number of flowers in gap. This species differs from Ch. vulvaria in having stem height, habit, blade shape of leaf, hairs length of leaf, hair density surface of leaf, smelling of decaying fish and leaf dorsal color. Information about the species, morphology, micromorphology, habitats and distribution is provided

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past's applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity

UAVs assessment in software-defined IoT networks: An overview

The technological advancements in the ubiquitous IoT era and the ever-growing desire of communities to enforce smart cities with security and safety of user data as their priority, mini Unmanned Aerial Vehicles (UAVs), or drones, are perceived as a tool for raising living standards by meeting the requirements of societies. Traditionally in UAV communication links, meshed ad hoc networks were among the first options of connectivity. However, the increased demand for deploying multi-UAV networks necessitates the development of a more robust and more secure networking infrastructure. In this regard, Software-Defined Networking (SDN) paradigm has proved to be the better alternative for multi-UAV communication since it can offer flexible services for management and control owing to its unique features such as decoupling control from UAVs and network programmability. Therefore, in this paper, we provide an overview of drone applications in SDN-enabled Drone Base Stations (DBS), surveillance monitoring and emergency networks, and review the performance assessment techniques and the associated cybersecurity aspects in these applications. Moreover, future research directions, after a thorough analysis of the literature, is presented in this paper. Through the development of an innovative and multifaceted drone performance-assessment framework with the primal concerns, that are meeting user-defined requirements and the provision of secure and reliable services, it is, therefore, necessary to advance in IoT-enabled spaces. We believe the present work is a step in the right direction, and it is essential for fastening the movement toward UAV-enabled smart cities

Fruit and Seed Anatomy of Chenopodium and Related Genera (Chenopodioideae, Chenopodiaceae/Amaranthaceae): Implications for Evolution and Taxonomy

A comparative carpological study of 96 species of all clades formerly considered as the tribe Chenopodieae has been conducted for the first time. The results show important differences in the anatomical structure of the pericarp and seed coat between representatives of terminal clades including Chenopodium s.str.+Chenopodiastrum and the recently recognized genera Blitum, Oxybasis and Dysphania. Within Chenopodium the most significant changes in fruit and seed structure are found in members of C. sect. Skottsbergia. The genera Rhagodia and Einadia differ insignificantly from Chenopodium. The evolution of heterospermy in Chenopodium is discussed. Almost all representatives of the tribe Dysphanieae are clearly separated from other Chenopodioideae on the basis of a diverse set of characteristics, including the small dimensions of the fruits (especially in Australian taxa), their subglobose shape (excl. Teloxys and Suckleya), and peculiarities of the pericarp indumentum. The set of fruit and seed characters evolved within the subfamily Chenopodioideae is described. A recent phylogenetic hypothesis is employed to examine the evolution of three (out of a total of 21) characters, namely seed color, testa-cell protoplast characteristics and embryo orientation